{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Wines Points prediction " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Import Libraries and Packages" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [] }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "import sys; sys.path.append('../')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import string\n", "import pandas as pd\n", "import numpy as np\n", "import plotly.express as px\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.linear_model import Ridge\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.model_selection import train_test_split, cross_val_score\n", "from sklearn.metrics import roc_auc_score, precision_score, recall_score, accuracy_score\n", "from sklearn.preprocessing import Normalizer\n", "from sklearn.feature_extraction.text import CountVectorizer\n", "from sklearn.feature_extraction.text import TfidfVectorizer" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[nltk_data] Downloading package stopwords to\n", "[nltk_data] /Users/urielkornfein/nltk_data...\n", "[nltk_data] Package stopwords is already up-to-date!\n", "[nltk_data] Downloading package punkt to\n", "[nltk_data] /Users/urielkornfein/nltk_data...\n", "[nltk_data] Package punkt is already up-to-date!\n", "[nltk_data] Downloading package averaged_perceptron_tagger to\n", "[nltk_data] /Users/urielkornfein/nltk_data...\n", "[nltk_data] Package averaged_perceptron_tagger is already up-to-\n", "[nltk_data] date!\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import nltk\n", "nltk.download('stopwords')\n", "nltk.download('punkt')\n", "nltk.download('averaged_perceptron_tagger')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from nltk.stem import PorterStemmer\n", "from nltk.tokenize import word_tokenize" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import mean_squared_error\n", "from sklearn.metrics import mean_absolute_error\n", "from sklearn.metrics import r2_score" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import cufflinks as cf\n", "cf.go_offline()\n", "cf.set_config_file(offline=False, world_readable=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we will try to predict the points a wine will get based on known characteristics (i.e. features, in the ML terminology). The mine point in this stage is to establish a simple, ideally super cost effective, basline.\n", "In the real world there is a tradeoff between complexity and perforamnce, and the DS job, among others, is to present a tradeoff tables of what performance is achivalbel at what complexity level. \n", "\n", "to which models with increased complexity and resource demands will be compared. Complexity should then be translated into cost. For example:\n", " * Compute cost \n", " * Maintenance cost\n", " * Serving costs (i.e. is new platform needed?) \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading the data" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# import pandas as pd\n", "# import cufflinks as cf; cf.go_offline()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(129971, 14)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_reviews = pd.read_csv(\"data/winemag-data-130k-v2.csv\")\n", "wine_reviews.shape" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0countrydescriptiondesignationpointspriceprovinceregion_1region_2taster_nametaster_twitter_handletitlevarietywinery
00ItalyAromas include tropical fruit, broom, brimston...Vulkà Bianco87NaNSicily & SardiniaEtnaNaNKerin O’Keefe@kerinokeefeNicosia 2013 Vulkà Bianco (Etna)White BlendNicosia
11PortugalThis is ripe and fruity, a wine that is smooth...Avidagos8715.0DouroNaNNaNRoger Voss@vossrogerQuinta dos Avidagos 2011 Avidagos Red (Douro)Portuguese RedQuinta dos Avidagos
22USTart and snappy, the flavors of lime flesh and...NaN8714.0OregonWillamette ValleyWillamette ValleyPaul Gregutt@paulgwineRainstorm 2013 Pinot Gris (Willamette Valley)Pinot GrisRainstorm
33USPineapple rind, lemon pith and orange blossom ...Reserve Late Harvest8713.0MichiganLake Michigan ShoreNaNAlexander PeartreeNaNSt. Julian 2013 Reserve Late Harvest Riesling ...RieslingSt. Julian
44USMuch like the regular bottling from 2012, this...Vintner's Reserve Wild Child Block8765.0OregonWillamette ValleyWillamette ValleyPaul Gregutt@paulgwineSweet Cheeks 2012 Vintner's Reserve Wild Child...Pinot NoirSweet Cheeks
\n", "
" ], "text/plain": [ " Unnamed: 0 country description \\\n", "0 0 Italy Aromas include tropical fruit, broom, brimston... \n", "1 1 Portugal This is ripe and fruity, a wine that is smooth... \n", "2 2 US Tart and snappy, the flavors of lime flesh and... \n", "3 3 US Pineapple rind, lemon pith and orange blossom ... \n", "4 4 US Much like the regular bottling from 2012, this... \n", "\n", " designation points price province \\\n", "0 Vulkà Bianco 87 NaN Sicily & Sardinia \n", "1 Avidagos 87 15.0 Douro \n", "2 NaN 87 14.0 Oregon \n", "3 Reserve Late Harvest 87 13.0 Michigan \n", "4 Vintner's Reserve Wild Child Block 87 65.0 Oregon \n", "\n", " region_1 region_2 taster_name \\\n", "0 Etna NaN Kerin O’Keefe \n", "1 NaN NaN Roger Voss \n", "2 Willamette Valley Willamette Valley Paul Gregutt \n", "3 Lake Michigan Shore NaN Alexander Peartree \n", "4 Willamette Valley Willamette Valley Paul Gregutt \n", "\n", " taster_twitter_handle title \\\n", "0 @kerinokeefe Nicosia 2013 Vulkà Bianco (Etna) \n", "1 @vossroger Quinta dos Avidagos 2011 Avidagos Red (Douro) \n", "2 @paulgwine  Rainstorm 2013 Pinot Gris (Willamette Valley) \n", "3 NaN St. Julian 2013 Reserve Late Harvest Riesling ... \n", "4 @paulgwine  Sweet Cheeks 2012 Vintner's Reserve Wild Child... \n", "\n", " variety winery \n", "0 White Blend Nicosia \n", "1 Portuguese Red Quinta dos Avidagos \n", "2 Pinot Gris Rainstorm \n", "3 Riesling St. Julian \n", "4 Pinot Noir Sweet Cheeks " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_reviews.head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0countrydescriptiondesignationpointspriceprovinceregion_1region_2taster_nametaster_twitter_handletitlevarietywinery
108587108587SpainEven the very best Txakoli won't appeal to eve...NaN8325.0Northern SpainGetariako TxakolinaNaNMichael Schachner@wineschachTxomín Etxaníz 2014 Rosé (Getariako Txakolina)RoséTxomín Etxaníz
4704747047FranceStarts off scents with toasted hazelnut that g...Domaine Mule Blanche8939.0Rhône ValleyCrozes-HermitageNaNJoe Czerwinski@JoeCzPaul Jaboulet Aîné 2007 Domaine Mule Blanche W...Rhône-style White BlendPaul Jaboulet Aîné
105999105999USMeritage wines must be Bordeaux blends; otherw...Genesis8716.0WashingtonColumbia Valley (WA)Columbia ValleyPaul Gregutt@paulgwineHogue 2007 Genesis Meritage (Columbia Valley (...MeritageHogue
7452974529ItalyFrom near the Tuscan coast comes this Bordeaux...Le Marze8940.0TuscanyToscanaNaNMichael Schachner@wineschachElisabetta 1998 Le Marze Red (Toscana)Bordeaux-style Red BlendElisabetta
5245552455USGreat balance in this fruit-forward yet well-s...NaN9018.0CaliforniaMendocino CountyNaNJim Gordon@gordone_cellarsGraziano 2011 Zinfandel (Mendocino County)ZinfandelGraziano
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" ], "text/plain": [ " Unnamed: 0 country description \\\n", "108587 108587 Spain Even the very best Txakoli won't appeal to eve... \n", "47047 47047 France Starts off scents with toasted hazelnut that g... \n", "105999 105999 US Meritage wines must be Bordeaux blends; otherw... \n", "74529 74529 Italy From near the Tuscan coast comes this Bordeaux... \n", "52455 52455 US Great balance in this fruit-forward yet well-s... \n", "\n", " designation points price province \\\n", "108587 NaN 83 25.0 Northern Spain \n", "47047 Domaine Mule Blanche 89 39.0 Rhône Valley \n", "105999 Genesis 87 16.0 Washington \n", "74529 Le Marze 89 40.0 Tuscany \n", "52455 NaN 90 18.0 California \n", "\n", " region_1 region_2 taster_name \\\n", "108587 Getariako Txakolina NaN Michael Schachner \n", "47047 Crozes-Hermitage NaN Joe Czerwinski \n", "105999 Columbia Valley (WA) Columbia Valley Paul Gregutt \n", "74529 Toscana NaN Michael Schachner \n", "52455 Mendocino County NaN Jim Gordon \n", "\n", " taster_twitter_handle \\\n", "108587 @wineschach \n", "47047 @JoeCz \n", "105999 @paulgwine  \n", "74529 @wineschach \n", "52455 @gordone_cellars \n", "\n", " title \\\n", "108587 Txomín Etxaníz 2014 Rosé (Getariako Txakolina) \n", "47047 Paul Jaboulet Aîné 2007 Domaine Mule Blanche W... \n", "105999 Hogue 2007 Genesis Meritage (Columbia Valley (... \n", "74529 Elisabetta 1998 Le Marze Red (Toscana) \n", "52455 Graziano 2011 Zinfandel (Mendocino County) \n", "\n", " variety winery \n", "108587 Rosé Txomín Etxaníz \n", "47047 Rhône-style White Blend Paul Jaboulet Aîné \n", "105999 Meritage Hogue \n", "74529 Bordeaux-style Red Blend Elisabetta \n", "52455 Zinfandel Graziano " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_reviews.sample(5)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "wine_reviews = wine_reviews.drop(columns=['Unnamed: 0'])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(119988, 13)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_reviews = wine_reviews.drop_duplicates()\n", "wine_reviews.shape" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 119988 entries, 0 to 129970\n", "Data columns (total 13 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 country 119929 non-null object \n", " 1 description 119988 non-null object \n", " 2 designation 85443 non-null object \n", " 3 points 119988 non-null int64 \n", " 4 price 111593 non-null float64\n", " 5 province 119929 non-null object \n", " 6 region_1 100428 non-null object \n", " 7 region_2 46769 non-null object \n", " 8 taster_name 95071 non-null object \n", " 9 taster_twitter_handle 90542 non-null object \n", " 10 title 119988 non-null object \n", " 11 variety 119987 non-null object \n", " 12 winery 119988 non-null object \n", "dtypes: float64(1), int64(1), object(11)\n", "memory usage: 12.8+ MB\n" ] } ], "source": [ "wine_reviews.info()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "#looks like there are na values, however, I'm choosing not to exclude them, as I assume,\n", "#according to the data structure, this won't hurt modeling that much" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "118840" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_reviews.title.nunique()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "#because there are less titles than rows, it seems some wines are tested twice or more" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countrydescriptiondesignationpointspriceprovinceregion_1region_2taster_nametaster_twitter_handletitlevarietywinery
3209USCreamy, lush and somewhat robust, this dry spa...Sonoma Brut9022.0CaliforniaSonoma CountySonomaVirginie Boone@vbooneGloria Ferrer NV Sonoma Brut Sparkling (Sonoma...Sparkling BlendGloria Ferrer
4399USMade predominantly from Pinot Noir, this is an...Sonoma Brut8822.0CaliforniaSonoma CountySonomaVirginie Boone@vbooneGloria Ferrer NV Sonoma Brut Sparkling (Sonoma...Sparkling BlendGloria Ferrer
27773USA wonderfully drinkable sparkling wine that ap...Sonoma Brut9020.0CaliforniaSonoma CountySonomaNaNNaNGloria Ferrer NV Sonoma Brut Sparkling (Sonoma...Sparkling BlendGloria Ferrer
63179USMade from mostly Pinot Noir grapes, with an ad...Sonoma Brut9222.0CaliforniaSonoma CountySonomaVirginie Boone@vbooneGloria Ferrer NV Sonoma Brut Sparkling (Sonoma...Sparkling BlendGloria Ferrer
81563USThis wine shows lots of finesse for the price....Sonoma Brut8924.0CaliforniaSonoma CountySonomaNaNNaNGloria Ferrer NV Sonoma Brut Sparkling (Sonoma...Sparkling BlendGloria Ferrer
94321USA bit rough and scouring in texture, this tast...Sonoma Brut8520.0CaliforniaSonoma CountySonomaNaNNaNGloria Ferrer NV Sonoma Brut Sparkling (Sonoma...Sparkling BlendGloria Ferrer
100738USA good, dry and elegant bubbly. Shows crisp fl...Sonoma Brut8920.0CaliforniaSonoma CountySonomaNaNNaNGloria Ferrer NV Sonoma Brut Sparkling (Sonoma...Sparkling BlendGloria Ferrer
109001USFrom almost 92% Pinot Noir with the remainder ...Sonoma Brut8822.0CaliforniaSonoma CountySonomaVirginie Boone@vbooneGloria Ferrer NV Sonoma Brut Sparkling (Sonoma...Sparkling BlendGloria Ferrer
122208USThis bubbly is rich in cherry and raspberry fr...Sonoma Brut8620.0CaliforniaSonoma CountySonomaNaNNaNGloria Ferrer NV Sonoma Brut Sparkling (Sonoma...Sparkling BlendGloria Ferrer
\n", "
" ], "text/plain": [ " country description \\\n", "3209 US Creamy, lush and somewhat robust, this dry spa... \n", "4399 US Made predominantly from Pinot Noir, this is an... \n", "27773 US A wonderfully drinkable sparkling wine that ap... \n", "63179 US Made from mostly Pinot Noir grapes, with an ad... \n", "81563 US This wine shows lots of finesse for the price.... \n", "94321 US A bit rough and scouring in texture, this tast... \n", "100738 US A good, dry and elegant bubbly. Shows crisp fl... \n", "109001 US From almost 92% Pinot Noir with the remainder ... \n", "122208 US This bubbly is rich in cherry and raspberry fr... \n", "\n", " designation points price province region_1 region_2 \\\n", "3209 Sonoma Brut 90 22.0 California Sonoma County Sonoma \n", "4399 Sonoma Brut 88 22.0 California Sonoma County Sonoma \n", "27773 Sonoma Brut 90 20.0 California Sonoma County Sonoma \n", "63179 Sonoma Brut 92 22.0 California Sonoma County Sonoma \n", "81563 Sonoma Brut 89 24.0 California Sonoma County Sonoma \n", "94321 Sonoma Brut 85 20.0 California Sonoma County Sonoma \n", "100738 Sonoma Brut 89 20.0 California Sonoma County Sonoma \n", "109001 Sonoma Brut 88 22.0 California Sonoma County Sonoma \n", "122208 Sonoma Brut 86 20.0 California Sonoma County Sonoma \n", "\n", " taster_name taster_twitter_handle \\\n", "3209 Virginie Boone @vboone \n", "4399 Virginie Boone @vboone \n", "27773 NaN NaN \n", "63179 Virginie Boone @vboone \n", "81563 NaN NaN \n", "94321 NaN NaN \n", "100738 NaN NaN \n", "109001 Virginie Boone @vboone \n", "122208 NaN NaN \n", "\n", " title variety \\\n", "3209 Gloria Ferrer NV Sonoma Brut Sparkling (Sonoma... Sparkling Blend \n", "4399 Gloria Ferrer NV Sonoma Brut Sparkling (Sonoma... Sparkling Blend \n", "27773 Gloria Ferrer NV Sonoma Brut Sparkling (Sonoma... Sparkling Blend \n", "63179 Gloria Ferrer NV Sonoma Brut Sparkling (Sonoma... Sparkling Blend \n", "81563 Gloria Ferrer NV Sonoma Brut Sparkling (Sonoma... Sparkling Blend \n", "94321 Gloria Ferrer NV Sonoma Brut Sparkling (Sonoma... Sparkling Blend \n", "100738 Gloria Ferrer NV Sonoma Brut Sparkling (Sonoma... Sparkling Blend \n", "109001 Gloria Ferrer NV Sonoma Brut Sparkling (Sonoma... Sparkling Blend \n", "122208 Gloria Ferrer NV Sonoma Brut Sparkling (Sonoma... Sparkling Blend \n", "\n", " winery \n", "3209 Gloria Ferrer \n", "4399 Gloria Ferrer \n", "27773 Gloria Ferrer \n", "63179 Gloria Ferrer \n", "81563 Gloria Ferrer \n", "94321 Gloria Ferrer \n", "100738 Gloria Ferrer \n", "109001 Gloria Ferrer \n", "122208 Gloria Ferrer " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_reviews[wine_reviews.title == wine_reviews.title.value_counts().idxmax()]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "#the reviews for the same title are different, based on reviewer and desc, so that should be ok" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 119988.000000\n", "mean 88.442236\n", "std 3.092915\n", "min 80.000000\n", "25% 86.000000\n", "50% 88.000000\n", "75% 91.000000\n", "max 100.000000\n", "Name: points, dtype: float64" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_reviews.points.describe()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "marker": { "color": "rgba(255, 153, 51, 0.6)", "line": { "color": "rgba(255, 153, 51, 1.0)", "width": 1 } }, "name": "points", "orientation": "v", "text": "", "type": "bar", "x": [ 88, 87, 90, 86, 89, 91, 85, 92, 84, 93, 94, 83, 82, 95, 81, 96, 80, 97, 98, 99, 100 ], "y": [ 15291, 15279, 13893, 11526, 11147, 10559, 9067, 8951, 6225, 6170, 3625, 2963, 1814, 1514, 689, 521, 397, 228, 77, 33, 19 ] } ], "layout": { "autosize": true, "legend": { "bgcolor": "#F5F6F9", "font": { "color": "#4D5663" } }, "paper_bgcolor": "#F5F6F9", "plot_bgcolor": "#F5F6F9", "shapes": [ { "line": { "color": "#db4052", "dash": "solid", "width": 1 }, "type": "line", "x0": 0, "x1": 1, "xref": "paper", "y0": 0, "y1": 0, "yref": "y" } ], "title": { "font": { "color": "#4D5663" }, "text": "Points distribution" }, "xaxis": { "autorange": true, "gridcolor": "#E1E5ED", "range": [ 79.5, 100.5 ], "showgrid": true, "tickfont": { "color": "#4D5663" }, "title": { "font": { "color": "#4D5663" }, "text": "" }, "type": "linear", "zerolinecolor": "#E1E5ED" }, "yaxis": { "autorange": true, "gridcolor": "#E1E5ED", "range": [ -44.841642228739005, 16098.149560117303 ], "showgrid": true, "tickfont": { "color": "#4D5663" }, "title": { "font": { "color": "#4D5663" }, "text": "" }, "type": "linear", "zerolinecolor": "#E1E5ED" } } }, "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "wine_reviews.points.value_counts().iplot(kind='bar', title = 'Points distribution', hline=(0))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "#points dist looks normal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### AVG Score and Price by Country" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countsproportion
US504570.420517
France203530.169625
Italy179400.149515
Spain61160.050972
Portugal52560.043804
\n", "
" ], "text/plain": [ " counts proportion\n", "US 50457 0.420517\n", "France 20353 0.169625\n", "Italy 17940 0.149515\n", "Spain 6116 0.050972\n", "Portugal 5256 0.043804" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.concat([wine_reviews.country.value_counts().rename('counts'), \n", " wine_reviews.country.value_counts().rename('proportion').div(wine_reviews.shape[0])], axis=1).head()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "def calc_average_score_and_price(country_df):\n", " return pd.Series({'average_price': country_df.price.mean(),\n", " 'average_score': country_df.points.mean()})" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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average_priceaverage_score
country
Argentina24.57526486.649831
Armenia14.50000087.500000
Australia35.68649188.561220
Austria31.33675190.129532
Bosnia and Herzegovina12.50000086.500000
\n", "
" ], "text/plain": [ " average_price average_score\n", "country \n", "Argentina 24.575264 86.649831\n", "Armenia 14.500000 87.500000\n", "Australia 35.686491 88.561220\n", "Austria 31.336751 90.129532\n", "Bosnia and Herzegovina 12.500000 86.500000" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats_by_country = wine_reviews.groupby('country').apply(calc_average_score_and_price)\n", "stats_by_country.head()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "marker": { "color": "rgba(255, 153, 51, 0.6)", "line": { "color": "rgba(255, 153, 51, 1.0)", "width": 1 } }, "name": "average_score", "orientation": "v", "text": "", "type": "bar", "x": [ "England", "India", "Austria", "Germany", "Canada", "Hungary", "China", "France", "Luxembourg", "Morocco", 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" ], "text/plain": [ " price points\n", "price 1.000000 0.416534\n", "points 0.416534 1.000000" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_reviews[['price','points']].corr(method = 'pearson')" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "price=%{x}
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "px.scatter(wine_reviews, x='price',y='points')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because prices are dependent on country, and point aren't, we should breakdown by country" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['US',\n", " 'France',\n", " 'Italy',\n", " 'Spain',\n", " 'Portugal',\n", " 'Chile',\n", " 'Argentina',\n", " 'Austria',\n", " 'Australia',\n", " 'Germany']" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top_ten_countries = wine_reviews.country.value_counts().index[:10]\n", "list(top_ten_countries)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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country
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points0.4553201.000000
Franceprice1.0000000.406553
points0.4065531.000000
Italyprice1.0000000.531854
points0.5318541.000000
Spainprice1.0000000.478852
points0.4788521.000000
Portugalprice1.0000000.438125
points0.4381251.000000
Chileprice1.0000000.482639
points0.4826391.000000
Argentinaprice1.0000000.571894
points0.5718941.000000
Austriaprice1.0000000.391295
points0.3912951.000000
Australiaprice1.0000000.468471
points0.4684711.000000
Germanyprice1.0000000.457733
points0.4577331.000000
\n", "
" ], "text/plain": [ " price points\n", "country \n", "US price 1.000000 0.455320\n", " points 0.455320 1.000000\n", "France price 1.000000 0.406553\n", " points 0.406553 1.000000\n", "Italy price 1.000000 0.531854\n", " points 0.531854 1.000000\n", "Spain price 1.000000 0.478852\n", " points 0.478852 1.000000\n", "Portugal price 1.000000 0.438125\n", " points 0.438125 1.000000\n", "Chile price 1.000000 0.482639\n", " points 0.482639 1.000000\n", "Argentina price 1.000000 0.571894\n", " points 0.571894 1.000000\n", "Austria price 1.000000 0.391295\n", " points 0.391295 1.000000\n", "Australia price 1.000000 0.468471\n", " points 0.468471 1.000000\n", "Germany price 1.000000 0.457733\n", " points 0.457733 1.000000" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_reviews.groupby('country')[['price','points']].corr(method = 'pearson').loc[top_ten_countries]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "country=Italy
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"text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "px.scatter(wine_reviews[wine_reviews.country.isin(top_ten_countries)], x='price',y='points',facet_col='country')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we can see that high score doesn't necessarily equal high price" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Points prediction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Points is descrete value target. There for we are talking about a prediction (Regression) problem (in contrary to classification problem). Prediction solutions can be measured in few metrics:\n", "\n", "* MSE - [Mean score error](https://en.wikipedia.org/wiki/Mean_squared_error)\n", "* R2 - [R Square](https://en.wikipedia.org/wiki/Coefficient_of_determination)\n", "* MAE - [Mean absolut error](https://en.wikipedia.org/wiki/Mean_absolute_error)\n", "\n", "Read more [here](https://towardsdatascience.com/what-are-the-best-metrics-to-evaluate-your-regression-model-418ca481755b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train and test set split" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To properly report results, let's split to train and test datasets:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "train_data = wine_reviews.sample(frac = 0.8)\n", "test_data = wine_reviews[~wine_reviews.index.isin(train_data.index)]\n", "assert(len(train_data) + len(test_data) == len(wine_reviews))" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(23998, 95990)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(test_data), len(train_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Baselines" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "# from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "def calc_prediction_quality(df, pred_score_col, true_score_col):\n", " return pd.Series({'MSE': mean_squared_error(df[true_score_col], df[pred_score_col]),\n", " 'MAE': mean_absolute_error(df[true_score_col], df[pred_score_col]),\n", " 'R2': r2_score(df[true_score_col], df[pred_score_col])})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Baseline 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The most basic baseline is simply the average points. The implementaion is as simple as:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "MSE 9.603218\n", "MAE 2.537841\n", "R2 -0.000018\n", "dtype: float64" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_data['basiline_1_predicted_points'] = train_data.points.mean()\n", "b1_stats = calc_prediction_quality(test_data, 'basiline_1_predicted_points', 'points')\n", "b1_stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Basline 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can probably improve by predicting the average score based on the origin country:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "country\n", "Argentina 86.684285\n", "Armenia 88.000000\n", "Australia 88.558338\n", "Austria 90.196701\n", "Bosnia and Herzegovina 86.500000\n", "Name: points, dtype: float64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "avg_points_by_country = train_data.groupby('country').points.mean()\n", "avg_points_by_country.head()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "MSE 9.130190\n", "MAE 2.471840\n", "R2 0.049241\n", "dtype: float64" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_data['basiline_2_predicted_points'] = test_data.country.map(avg_points_by_country).fillna(train_data.points.mean())\n", "b2_stats = calc_prediction_quality(test_data, 'basiline_2_predicted_points', 'points')\n", "b2_stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Baseline 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adding more breakdowns will increase our granularity but can result in overfitting. Yet:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "country province \n", "Argentina Mendoza Province 86.800329\n", " Other 85.985149\n", "Armenia Armenia 88.000000\n", "Australia Australia Other 85.451429\n", " New South Wales 87.666667\n", "Name: basiline_3_predicted_points, dtype: float64" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "avg_points_by_country_and_region = train_data.groupby(['country','province']).points.mean().rename('basiline_3_predicted_points')\n", "avg_points_by_country_and_region.head()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((23998, 16), (23998, 15))" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_data_with_baseline_3 = test_data.merge(avg_points_by_country_and_region, on = ['country','province'], how='left')\n", "test_data_with_baseline_3.basiline_3_predicted_points = test_data_with_baseline_3.basiline_3_predicted_points.fillna(test_data_with_baseline_3.basiline_2_predicted_points).fillna(test_data.basiline_1_predicted_points)\n", "test_data_with_baseline_3.shape, test_data.shape" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "MSE 8.589486\n", "MAE 2.376368\n", "R2 0.105546\n", "dtype: float64" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b3_stats = calc_prediction_quality(test_data_with_baseline_3, 'basiline_3_predicted_points', 'points')\n", "b3_stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Baselines summary" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MSEMAER2
baseline_19.6032182.537841-0.000018
baseline_29.1301902.4718400.049241
baseline_38.5894862.3763680.105546
\n", "
" ], "text/plain": [ " MSE MAE R2\n", "baseline_1 9.603218 2.537841 -0.000018\n", "baseline_2 9.130190 2.471840 0.049241\n", "baseline_3 8.589486 2.376368 0.105546" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "baseline_summary = pd.DataFrame([b1_stats, b2_stats, b3_stats], index=['baseline_1', 'baseline_2','baseline_3'])\n", "baseline_summary" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "baseline_summary.to_csv('data/baselines_summary.csv', index=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training a Boosting trees regressor" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import LabelEncoder\n", "from sklearn.preprocessing import StandardScaler\n", "le = LabelEncoder()\n", "scalar=StandardScaler()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Preparing data - Lable encoding categorical features" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "categorical_features = ['country','province','region_1','region_2','taster_name','variety','winery']\n", "numerical_features = ['price']\n", "features = categorical_features + numerical_features" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countryprovinceregion_1region_2taster_namevarietywinerypricepoints
0223324246969111608-1.087
1321087386164511295615.087
241269121817154371301814.087
341218549604801439013.087
441269121817154411462165.087
\n", "
" ], "text/plain": [ " country province region_1 region_2 taster_name variety winery price \\\n", "0 22 332 424 6 9 691 11608 -1.0 \n", "1 32 108 738 6 16 451 12956 15.0 \n", "2 41 269 1218 17 15 437 13018 14.0 \n", "3 41 218 549 6 0 480 14390 13.0 \n", "4 41 269 1218 17 15 441 14621 65.0 \n", "\n", " points \n", "0 87 \n", "1 87 \n", "2 87 \n", "3 87 \n", "4 87 " ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "encoded_features = wine_reviews[categorical_features].apply(lambda col: le.fit_transform(col.fillna('NA')))\n", "encoded_features['price'] = wine_reviews.price.fillna(-1)\n", "encoded_features['points'] = wine_reviews.points\n", "encoded_features.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Re-splitting to train and test" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countryprovinceregion_1region_2taster_namevarietywinerypricepoints
0223324246969111608-1.087
441269121817154411462165.087
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13223324246938810819-1.087
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..............................
1299380216589612280275825.091
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129951151121616209843930.090
129965151121616437610028.090
\n", "

23998 rows × 9 columns

\n", "
" ], "text/plain": [ " country province region_1 region_2 taster_name variety winery \\\n", "0 22 332 424 6 9 691 11608 \n", "4 41 269 1218 17 15 441 14621 \n", "5 38 263 758 6 12 591 14706 \n", "13 22 332 424 6 9 388 10819 \n", "14 41 51 185 1 11 125 11125 \n", "... ... ... ... ... ... ... ... \n", "129938 0 216 589 6 12 280 2758 \n", "129945 41 51 974 1 11 137 1274 \n", "129946 17 232 738 6 1 480 1277 \n", "129951 15 11 21 6 16 209 8439 \n", "129965 15 11 21 6 16 437 6100 \n", "\n", " price points \n", "0 -1.0 87 \n", "4 65.0 87 \n", "5 15.0 87 \n", "13 -1.0 87 \n", "14 12.0 87 \n", "... ... ... \n", "129938 25.0 91 \n", "129945 20.0 90 \n", "129946 17.0 90 \n", "129951 30.0 90 \n", "129965 28.0 90 \n", "\n", "[23998 rows x 9 columns]" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_encoded_features = encoded_features[encoded_features.index.isin(train_data.index)]\n", "test_encoded_features = encoded_features[encoded_features.index.isin(test_data.index)]\n", "assert(len(train_encoded_features) + len(test_encoded_features) == len(wine_reviews))\n", "test_encoded_features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Fitting a tree-regressor" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "from src.models import i_feel_lucky_xgboost_training" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 95990 entries, 1 to 129970\n", "Data columns (total 9 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 country 95990 non-null int64 \n", " 1 province 95990 non-null int64 \n", " 2 region_1 95990 non-null int64 \n", " 3 region_2 95990 non-null int64 \n", " 4 taster_name 95990 non-null int64 \n", " 5 variety 95990 non-null int64 \n", " 6 winery 95990 non-null int64 \n", " 7 price 95990 non-null float64\n", " 8 points 95990 non-null int64 \n", "dtypes: float64(1), int64(8)\n", "memory usage: 7.3 MB\n" ] } ], "source": [ "train_encoded_features.info()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "xgb_clf, clf_name = i_feel_lucky_xgboost_training(train_encoded_features, test_encoded_features, features, 'points', name='xgb_clf_points_prediction')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at the function output - specifically the **xgb_clf_points_prediction** column:" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countryprovinceregion_1region_2taster_namevarietywinerypricepointsxgb_clf_points_prediction
0223324246969111608-1.08787
441269121817154411462165.08792
5382637586125911470615.08787
13223324246938810819-1.08787
1441511851111251112512.08787
\n", "
" ], "text/plain": [ " country province region_1 region_2 taster_name variety winery \\\n", "0 22 332 424 6 9 691 11608 \n", "4 41 269 1218 17 15 441 14621 \n", "5 38 263 758 6 12 591 14706 \n", "13 22 332 424 6 9 388 10819 \n", "14 41 51 185 1 11 125 11125 \n", "\n", " price points xgb_clf_points_prediction \n", "0 -1.0 87 87 \n", "4 65.0 87 92 \n", "5 15.0 87 87 \n", "13 -1.0 87 87 \n", "14 12.0 87 87 " ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_encoded_features.head()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "MSE 6.410284\n", "MAE 1.907826\n", "R2 0.332474\n", "dtype: float64" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xgb_stats = calc_prediction_quality(test_encoded_features, 'xgb_clf_points_prediction','points')\n", "xgb_stats" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MSEMAER2
baseline_19.6032182.537841-0.000018
baseline_29.1301902.4718400.049241
baseline_38.5894862.3763680.105546
regression_by_xgb6.4102841.9078260.332474
\n", "
" ], "text/plain": [ " MSE MAE R2\n", "baseline_1 9.603218 2.537841 -0.000018\n", "baseline_2 9.130190 2.471840 0.049241\n", "baseline_3 8.589486 2.376368 0.105546\n", "regression_by_xgb 6.410284 1.907826 0.332474" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_compared = pd.DataFrame([b1_stats, b2_stats, b3_stats, xgb_stats], index=['baseline_1', 'baseline_2','baseline_3','regression_by_xgb'])\n", "all_compared" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "all_compared.to_csv('data/all_models_compared.csv', index=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Classical NLP approaches" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using only the text from the \"description\" column" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Removing stop words and punctuation" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "stopwords = nltk.corpus.stopwords.words(\"english\") + nltk.corpus.stopwords.words(\"italian\") + nltk.corpus.stopwords.words(\"spanish\") + nltk.corpus.stopwords.words(\"french\")" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "from src.clean import leave_language_only" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "#cleaning desc column\n", "\n", "train_data['description_clean'] = leave_language_only(train_data.description.str.lower())\n", "test_data['description_clean'] = leave_language_only(test_data.description.str.lower())" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "all_test_words = [word.lower() for description in train_data.description_clean for word in nltk.word_tokenize(description) if word.lower() not in stopwords and word not in string.punctuation ]\n", "# Words without stop words" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Creating Vocabulary" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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wordwordCountwordImportance
0wine577290.000012
1flavors462480.000015
2fruit332680.000021
3aromas289330.000024
4palate278540.000025
5finish257950.000027
6acidity255960.000027
7tannins228050.000030
8drink220640.000031
9cherry201880.000034
\n", "
" ], "text/plain": [ " word wordCount wordImportance\n", "0 wine 57729 0.000012\n", "1 flavors 46248 0.000015\n", "2 fruit 33268 0.000021\n", "3 aromas 28933 0.000024\n", "4 palate 27854 0.000025\n", "5 finish 25795 0.000027\n", "6 acidity 25596 0.000027\n", "7 tannins 22805 0.000030\n", "8 drink 22064 0.000031\n", "9 cherry 20188 0.000034" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "words_df = pd.DataFrame(data = all_test_words, columns = ['word']).word.value_counts().reset_index()\n", "words_df.columns = ['word','wordCount']\n", "words_df['wordImportance'] = len(words_df) / words_df.wordCount / words_df.wordCount.max()\n", "words_df.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we can see that wine, flavors and drink are improtant but probably add nothing of value as they will repeat in most descriptions.\n", "regardless of the score - so we will remove them by adding them to the stop words." ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "stopwords.append('wine')\n", "stopwords.append('flavors')\n", "stopwords.append('drink')" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "all_test_words = [word.lower() for description in train_data.description_clean for word in nltk.word_tokenize(description) if word.lower() not in stopwords and word not in string.punctuation ]" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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wordwordCountwordImportance
0fruit332680.000036
1aromas289330.000042
2palate278540.000043
3finish257950.000047
4acidity255960.000047
5tannins228050.000053
6cherry201880.000060
7ripe199280.000060
8black188030.000064
9notes140620.000086
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" ], "text/plain": [ " word wordCount wordImportance\n", "0 fruit 33268 0.000036\n", "1 aromas 28933 0.000042\n", "2 palate 27854 0.000043\n", "3 finish 25795 0.000047\n", "4 acidity 25596 0.000047\n", "5 tannins 22805 0.000053\n", "6 cherry 20188 0.000060\n", "7 ripe 19928 0.000060\n", "8 black 18803 0.000064\n", "9 notes 14062 0.000086" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "words_df = pd.DataFrame(data = all_test_words, columns = ['word']).word.value_counts().reset_index()\n", "words_df.columns = ['word','wordCount']\n", "words_df['wordImportance'] = len(words_df) / words_df.wordCount / words_df.wordCount.max()\n", "words_df.head(10)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total of 2233532 words, 40083 unique words\n" ] } ], "source": [ "print(\"Total of {} words, {} unique words\".format(len(all_test_words), len(words_df)))" ] }, { 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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "words_df.set_index('word').wordCount.head(20).iplot(kind = 'bar', title = 'Most frequent words in Corpus', yTitle = 'Count', xTitle = 'Word')" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of unique words in training dataset: 40083\n", "Using words with 2 or more appearnces will reduce the corpus size to: 8002\n", "propotion of new vocabulary from all training unique words is 0.200\n", "Number of records in training dataset: 95990\n", "features vs records in training dataset: 0.083\n" ] } ], "source": [ "new_size = words_df[words_df.wordCount >= 10].shape[0]\n", "tr_size = words_df.shape[0]\n", "tr_rec = train_data.shape[0]\n", "print(\"Number of unique words in training dataset: {}\".format(tr_size))\n", "print(\"Using words with 2 or more appearnces will reduce the corpus size to: {}\".format(new_size))\n", "print(f'propotion of new vocabulary from all training unique words is {new_size/tr_size:.3f}')\n", "print(\"Number of records in training dataset: {}\".format(tr_rec))\n", "print(f'features vs records in training dataset: {new_size/tr_rec:.3f}')" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of unique words in training dataset: 40083\n", "Using words with 5 or more appearnces will reduce the corpus size to: 11715\n", "propotion of new vocabulary from all training unique words is 0.292\n", "Number of records in training dataset: 95990\n", "features vs records in training dataset: 0.122\n" ] } ], "source": [ "new_size = words_df[words_df.wordCount >= 5].shape[0]\n", "tr_size = words_df.shape[0]\n", "tr_rec = train_data.shape[0]\n", "print(\"Number of unique words in training dataset: {}\".format(tr_size))\n", "print(\"Using words with 5 or more appearnces will reduce the corpus size to: {}\".format(new_size))\n", "print(f'propotion of new vocabulary from all training unique words is {new_size/tr_size:.3f}')\n", "print(\"Number of records in training dataset: {}\".format(tr_rec))\n", "print(f'features vs records in training dataset: {new_size/tr_rec:.3f}')" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "words_df = words_df[words_df.wordCount >= 5]" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(95990, 11715)" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clean_vocab = set(words_df.word)\n", "count_vect = CountVectorizer(vocabulary = clean_vocab, tokenizer=nltk.word_tokenize)\n", "clean_bow_counts = count_vect.fit_transform(train_data.description_clean)\n", "clean_bow_counts.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Using train BOW to run a CV on it and find optimal alpha" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "search_grid = np.logspace(-2, 4, num=50, endpoint=True, base=10.0)\n", "mse_by_alpha = []\n", "for alpha in search_grid:\n", " model = Ridge( alpha=alpha)\n", " avg_score = abs(cross_val_score(model, clean_bow_counts, y=train_data.points, cv=10, scoring='neg_mean_squared_error').mean())\n", " mse_by_alpha.append((alpha,avg_score))" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "line": { "color": "rgba(255, 153, 51, 1.0)", "dash": "solid", "shape": "linear", "width": 3 }, "mode": "lines", "name": "MSE", "text": "", "type": "scatter", "x": [ 0.01, 0.013257113655901088, 0.017575106248547922, 0.023299518105153717, 0.030888435964774818, 0.040949150623804255, 0.054286754393238594, 0.07196856730011521, 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3.036515304706321, 3.023473845003599, 3.01524370156497, 3.0119472000362455, 3.014024082502401, 3.0219510643165286, 3.03574258909342, 3.0558507998228137, 3.0829218119158415, 3.1174304688648964, 3.1600140643766763, 3.2114985721354534 ] } ], "layout": { "autosize": true, "legend": { "bgcolor": "#F5F6F9", "font": { "color": "#4D5663" } }, "paper_bgcolor": "#F5F6F9", "plot_bgcolor": "#F5F6F9", "shapes": [ { "line": { "color": "#db4052", "dash": "solid", "width": 1 }, "type": "line", "x0": 0, "x1": 1, "xref": "paper", "y0": [ 0, 0 ], "y1": [ 0, 0 ], "yref": "y" } ], "title": { "font": { "color": "#4D5663" }, "text": "BOW Counts - MSE as a function of learning rate (alpha)" }, "xaxis": { "autorange": true, "gridcolor": "#E1E5ED", "range": [ 0.01, 145.63484775012444 ], "showgrid": true, "tickfont": { "color": "#4D5663" }, "title": { "font": { "color": "#4D5663" }, "text": "alpha" }, "type": "linear", "zerolinecolor": "#E1E5ED" }, "yaxis": { "autorange": true, "gridcolor": "#E1E5ED", "range": [ 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cv_results = pd.DataFrame(mse_by_alpha, columns = ['alpha', 'MSE'])\n", "cv_results.query('alpha < 150').set_index('alpha').MSE.iplot(title = 'BOW Counts - MSE as a function of learning rate (alpha)', xTitle = 'alpha', yTitle = 'MSE', width = 3, hline=(0,0))" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " alpha MSE\n", "26 15.264180 3.011947\n", "27 20.235896 3.014024\n", "25 11.513954 3.015244\n", "28 26.826958 3.021951\n", "24 8.685114 3.023474" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cv_results.sort_values(by='MSE').head(5)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "15.264179671752334 3.0119472000362455\n" ] } ], "source": [ "opt_alpha, max_auc = cv_results.loc[cv_results.MSE.idxmin()]\n", "print(opt_alpha, max_auc)" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Ridge(alpha=15.264179671752334)" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = Ridge( alpha=opt_alpha)\n", "model.fit(clean_bow_counts, train_data.points)" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "clean_bow_counts_test = count_vect.fit_transform(test_data.description_clean)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [], "source": [ "test_data['predicted_score']= model.predict(clean_bow_counts_test)" ] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "MSE 3.009492\n", "MAE 1.367681\n", "R2 0.686611\n", "dtype: float64" ] }, "execution_count": 132, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bow_stats = calc_prediction_quality(test_data, 'predicted_score', 'points')\n", "bow_stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Creating sparse matrix for BOW + Features" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [], "source": [ "from scipy.sparse import coo_matrix, hstack\n", "\n", "train_features_spares_matrix = coo_matrix(train_encoded_features.iloc[:,0:8])\n", "train_united_features = hstack((clean_bow_counts ,train_features_spares_matrix))\n", "\n", "test_features_spares_matrix = coo_matrix(test_encoded_features.iloc[:,0:8])\n", "test_united_features = hstack((clean_bow_counts_test ,test_features_spares_matrix))" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [], "source": [ "%%capture\n", "search_grid=[0.001,0.01,0.1,0,1,5,15,50,100,1000]\n", "auc_by_alpha = []\n", "for alpha in search_grid:\n", " model = Ridge( alpha=alpha,tol=0.00001)\n", " avg_score = abs(cross_val_score(model, train_united_features, y =train_data.points, cv = 10, scoring = 'neg_mean_squared_error').mean())\n", " auc_by_alpha.append((alpha,avg_score))" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(0.001, 3.3152235654677114),\n", " (0.01, 3.3133731200303815),\n", " (0.1, 3.2961926207362198),\n", " (0, 3.3154180930630575),\n", " (1, 3.1894347133394705),\n", " (5, 3.053561182472134),\n", " (15, 3.0121783482544187),\n", " (50, 3.0612833164881055),\n", " (100, 3.145244992612929),\n", " (1000, 3.881141406454746)]" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "auc_by_alpha" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "line": { "color": "rgba(255, 153, 51, 1.0)", "dash": "solid", "shape": "linear", "width": 3 }, "mode": "lines", "name": "MSE", "text": "", "type": "scatter", "x": [ 0.001, 0.01, 0.1, 0, 1, 5, 15, 50, 100 ], "y": [ 3.3152235654677114, 3.3133731200303815, 3.2961926207362198, 3.3154180930630575, 3.1894347133394705, 3.053561182472134, 3.0121783482544187, 3.0612833164881055, 3.145244992612929 ] } ], "layout": { "autosize": true, "legend": { "bgcolor": "#F5F6F9", "font": { "color": "#4D5663" } }, "paper_bgcolor": "#F5F6F9", "plot_bgcolor": "#F5F6F9", "shapes": [ { "line": { "color": "#db4052", "dash": "solid", "width": 1 }, "type": "line", "x0": 0, "x1": 1, "xref": "paper", "y0": [ 0, 0 ], "y1": [ 0, 0 ], "yref": "y" } ], "title": { "font": { "color": "#4D5663" }, "text": "BOW Counts - MSE as a function of learning rate (alpha)" }, "xaxis": { "autorange": true, "gridcolor": "#E1E5ED", "range": [ 0, 100 ], "showgrid": true, "tickfont": { "color": "#4D5663" }, "title": { "font": { "color": "#4D5663" }, "text": "alpha" }, "type": "linear", "zerolinecolor": "#E1E5ED" }, "yaxis": { "autorange": true, "gridcolor": "#E1E5ED", "range": [ 2.9953316957650498, 3.3322647455524264 ], "showgrid": true, "tickfont": { "color": "#4D5663" }, "title": { "font": { "color": "#4D5663" }, "text": "MSE" }, "type": "linear", "zerolinecolor": "#E1E5ED" } } }, "image/png": 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"text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cv_results = pd.DataFrame(auc_by_alpha, columns = ['alpha', 'MSE'])\n", "fig=cv_results.query('alpha <= 100').set_index('alpha').MSE.iplot(title = 'BOW Counts - MSE as a function of learning rate (alpha)', xTitle = 'alpha', yTitle = 'MSE', width = 3, hline=(0,0))" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "15.0 3.0121783482544187\n" ] } ], "source": [ "opt_alpha, min_mse = cv_results.loc[cv_results.MSE.idxmin()]\n", "print(opt_alpha, min_mse)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Ridge(alpha=15.0, max_iter=10000, tol=1e-05)" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = Ridge( alpha=opt_alpha,max_iter=10000,tol=0.00001)\n", "model.fit(train_united_features, train_data.points)" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [], "source": [ "test_data['predicted_score_by_both']= model.predict(test_united_features)" ] }, { "cell_type": "code", "execution_count": 133, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "MSE 3.013434\n", "MAE 1.368512\n", "R2 0.686200\n", "dtype: float64" ] }, "execution_count": 133, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bowf_stats = calc_prediction_quality(test_data, 'predicted_score_by_both', 'points')\n", "bowf_stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Deep Learning approaches" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fully connected network on the text only" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Tokenization" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow.keras.layers import TextVectorization, Embedding, Dense, GlobalAveragePooling1D, Dropout\n", "from tensorflow.keras.callbacks import EarlyStopping" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is a good size for the vocabulary? " ] }, { "cell_type": "code", "execution_count": 136, "metadata": {}, "outputs": [], "source": [ "wine_reviews['description_clean'] = leave_language_only(wine_reviews.description.str.lower())" ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.95 68.0\n", "0.99 81.0\n", "Name: description_clean, dtype: float64" ] }, "execution_count": 137, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_reviews.description_clean.apply(lambda x: len(x.split(' '))).quantile([0.95, 0.99])" ] }, { "cell_type": "code", "execution_count": 138, "metadata": {}, "outputs": [], "source": [ "vocab_size = 32000\n", "sequence_length = 60\n", "\n", "# Use the text vectorization layer to normalize, split, and map strings to integers. Set maximum_sequence length as all samples are not of the same length.\n", "vectorize_layer = TextVectorization(\n", " #standardize=lambda text: tf.strings.lower(text), # You can use your own normalization function here\n", " standardize='lower_and_strip_punctuation', # Or you can use a pre-made normalization function\n", " max_tokens=vocab_size, \n", " split='whitespace',\n", " output_mode='int',\n", " name = 'Text_processing',\n", " output_sequence_length=sequence_length)" ] }, { "cell_type": "code", "execution_count": 139, "metadata": {}, "outputs": [], "source": [ "vectorize_layer.adapt(train_data['description_clean'])" ] }, { "cell_type": "code", "execution_count": 140, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "lots of ripe forward citrus tropical fruit melon and peach flavors in this slightly sweet sauvignon blanc a bit of oak brings a nice smoky creaminess more controversial is the green gooseberry tartness that will divide wine lovers into pro and con \n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 140, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_description = train_data['description_clean'].sample().iloc[0]\n", "print(sample_description)\n", "vectorize_layer(sample_description)" ] }, { "cell_type": "code", "execution_count": 141, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "404 ---> lots\n", "5 ---> of\n", "27 ---> ripe\n", "354 ---> forward\n", "61 ---> citrus\n", "210 ---> tropical\n", "16 ---> fruit\n", "179 ---> melon\n", "2 ---> and\n", "79 ---> peach\n", "10 ---> flavors\n", "11 ---> in\n", "7 ---> this\n", "150 ---> slightly\n", "53 ---> sweet\n", "91 ---> sauvignon\n", "255 ---> blanc\n", "4 ---> a\n", "120 ---> bit\n", "5 ---> of\n" ] } ], "source": [ "for token in vectorize_layer(sample_description).numpy()[:20]:\n", " print(f\"{token} ---> \",vectorize_layer.get_vocabulary()[token])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Modeling" ] }, { "cell_type": "code", "execution_count": 142, "metadata": {}, "outputs": [], "source": [ "embedding_dim=16\n", "\n", "model = tf.keras.Sequential([\n", " tf.keras.Input(shape=(1,), dtype=tf.string),\n", " vectorize_layer,\n", " Embedding(vocab_size, embedding_dim, name=\"embedding\"),\n", " GlobalAveragePooling1D(),\n", " Dense(164, activation='tanh', name='hidden_layer'),\n", " Dropout(0.2),\n", " Dense(1, name = 'output_layer')\n", "])" ] }, { "cell_type": "code", "execution_count": 143, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_4\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " Text_processing (TextVector (None, 60) 0 \n", " ization) \n", " \n", " embedding (Embedding) (None, 60, 16) 512000 \n", " \n", " global_average_pooling1d_2 (None, 16) 0 \n", " (GlobalAveragePooling1D) \n", " \n", " hidden_layer (Dense) (None, 164) 2788 \n", " \n", " dropout_4 (Dropout) (None, 164) 0 \n", " \n", " output_layer (Dense) (None, 1) 165 \n", " \n", "=================================================================\n", "Total params: 514,953\n", "Trainable params: 514,953\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model.summary()" ] }, { "cell_type": "code", "execution_count": 144, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 144, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tf.keras.utils.plot_model(model, show_dtype=False, show_shapes=True, show_layer_names=True)" ] }, { "cell_type": "code", "execution_count": 145, "metadata": {}, "outputs": [], "source": [ "model.compile(\n", " optimizer=tf.optimizers.Adam(), loss='mean_absolute_error', metrics=['mean_squared_error','mean_absolute_error'])" ] }, { "cell_type": "code", "execution_count": 146, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "750/750 [==============================] - 4s 5ms/step - loss: 22.0045 - mean_squared_error: 1360.6184 - mean_absolute_error: 22.0045 - val_loss: 2.4706 - val_mean_squared_error: 9.2027 - val_mean_absolute_error: 2.4706\n", "Epoch 2/20\n", "750/750 [==============================] - 3s 4ms/step - loss: 3.4415 - mean_squared_error: 18.5775 - mean_absolute_error: 3.4415 - val_loss: 1.7236 - val_mean_squared_error: 4.6598 - val_mean_absolute_error: 1.7236\n", "Epoch 3/20\n", "750/750 [==============================] - 4s 5ms/step - loss: 3.1687 - mean_squared_error: 15.8480 - mean_absolute_error: 3.1687 - val_loss: 1.5280 - val_mean_squared_error: 3.7224 - val_mean_absolute_error: 1.5280\n", "Epoch 4/20\n", "750/750 [==============================] - 4s 5ms/step - loss: 3.1245 - mean_squared_error: 15.3364 - mean_absolute_error: 3.1245 - val_loss: 1.4768 - val_mean_squared_error: 3.4951 - val_mean_absolute_error: 1.4768\n", "Epoch 5/20\n", "750/750 [==============================] - 3s 5ms/step - loss: 3.0937 - mean_squared_error: 15.0635 - mean_absolute_error: 3.0937 - val_loss: 1.4429 - val_mean_squared_error: 3.3335 - val_mean_absolute_error: 1.4429\n", "Epoch 6/20\n", "750/750 [==============================] - 3s 4ms/step - loss: 3.0595 - mean_squared_error: 14.7270 - mean_absolute_error: 3.0595 - val_loss: 1.4507 - val_mean_squared_error: 3.3742 - val_mean_absolute_error: 1.4507\n", "Epoch 7/20\n", "750/750 [==============================] - 4s 5ms/step - loss: 3.0393 - mean_squared_error: 14.5730 - mean_absolute_error: 3.0393 - val_loss: 1.4277 - val_mean_squared_error: 3.2451 - val_mean_absolute_error: 1.4277\n", "Epoch 8/20\n", "750/750 [==============================] - 4s 5ms/step - loss: 3.0122 - mean_squared_error: 14.3784 - mean_absolute_error: 3.0122 - val_loss: 1.4158 - val_mean_squared_error: 3.2084 - val_mean_absolute_error: 1.4158\n", "Epoch 9/20\n", "750/750 [==============================] - 3s 5ms/step - loss: 3.0369 - mean_squared_error: 14.5155 - mean_absolute_error: 3.0369 - val_loss: 1.4030 - val_mean_squared_error: 3.1452 - val_mean_absolute_error: 1.4030\n", "Epoch 10/20\n", "750/750 [==============================] - 4s 5ms/step - loss: 3.0042 - mean_squared_error: 14.2364 - mean_absolute_error: 3.0042 - val_loss: 1.3950 - val_mean_squared_error: 3.1123 - val_mean_absolute_error: 1.3950\n", "Epoch 11/20\n", "750/750 [==============================] - 3s 4ms/step - loss: 2.9899 - mean_squared_error: 14.0874 - mean_absolute_error: 2.9899 - val_loss: 1.3927 - val_mean_squared_error: 3.1012 - val_mean_absolute_error: 1.3927\n", "Epoch 12/20\n", "750/750 [==============================] - 3s 4ms/step - loss: 2.9875 - mean_squared_error: 14.1035 - mean_absolute_error: 2.9875 - val_loss: 1.4086 - val_mean_squared_error: 3.1789 - val_mean_absolute_error: 1.4086\n", "Epoch 13/20\n", "750/750 [==============================] - 3s 5ms/step - loss: 2.9719 - mean_squared_error: 13.9209 - mean_absolute_error: 2.9719 - val_loss: 1.3849 - val_mean_squared_error: 3.0737 - val_mean_absolute_error: 1.3849\n", "Epoch 14/20\n", "750/750 [==============================] - 3s 4ms/step - loss: 2.9796 - mean_squared_error: 13.9607 - mean_absolute_error: 2.9796 - val_loss: 1.3791 - val_mean_squared_error: 3.0526 - val_mean_absolute_error: 1.3791\n", "Epoch 15/20\n", "750/750 [==============================] - 3s 4ms/step - loss: 2.9654 - mean_squared_error: 13.8495 - mean_absolute_error: 2.9654 - val_loss: 1.4127 - val_mean_squared_error: 3.2022 - val_mean_absolute_error: 1.4127\n", "Epoch 16/20\n", "750/750 [==============================] - 3s 4ms/step - loss: 2.9551 - mean_squared_error: 13.7694 - mean_absolute_error: 2.9551 - val_loss: 1.3854 - val_mean_squared_error: 3.0830 - val_mean_absolute_error: 1.3854\n", "CPU times: user 2min 13s, sys: 16.5 s, total: 2min 29s\n", "Wall time: 55.6 s\n" ] } ], "source": [ "%%time\n", "text_col, target_col = 'description_clean', 'points'\n", "\n", "early_stopping_monitor = EarlyStopping(\n", " monitor='val_mean_squared_error',\n", " min_delta=0,\n", " patience=2,\n", " verbose=0,\n", " restore_best_weights=True\n", ")\n", "\n", "history = model.fit(\n", " train_data[text_col],\n", " train_data[target_col],\n", " epochs=20,\n", " batch_size=128,\n", " verbose=1, \n", " callbacks=[early_stopping_monitor],\n", " validation_data = (test_data[text_col], test_data[target_col]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simple NN Prediction & Evaluation" ] }, { "cell_type": "code", "execution_count": 147, "metadata": {}, "outputs": [], "source": [ "test_data['dnn_prediction'] = model.predict(test_data[text_col])" ] }, { "cell_type": "code", "execution_count": 148, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "MSE 3.052601\n", "MAE 1.379098\n", "R2 0.682122\n", "dtype: float64" ] }, "execution_count": 148, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dnn_stats = calc_prediction_quality(test_data, 'dnn_prediction', target_col)\n", "dnn_stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Descriptions words concatinated" ] }, { "cell_type": "code", "execution_count": 149, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow.keras.layers import Reshape, Dense, Dropout, Embedding\n", "from tensorflow.keras import Sequential" ] }, { "cell_type": "code", "execution_count": 150, "metadata": {}, "outputs": [], "source": [ "embedding_dim=16\n", "\n", "model = tf.keras.Sequential([\n", " tf.keras.Input(shape=(1,), dtype=tf.string),\n", " vectorize_layer,\n", " Embedding(vocab_size, embedding_dim, name=\"embedding\", ),\n", " Reshape((embedding_dim * sequence_length, ), name='concat_words'),\n", " Dense(164, activation='tanh', name='hidden_layer'),\n", " Dropout(0.7),\n", " Dense(1, name = 'output_layer')\n", "])" ] }, { "cell_type": "code", "execution_count": 151, "metadata": {}, "outputs": [], "source": [ "model.compile(\n", " optimizer=tf.optimizers.Adam(), loss='mean_absolute_error', metrics=['mean_squared_error','mean_absolute_error'])" ] }, { "cell_type": "code", "execution_count": 152, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 152, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tf.keras.utils.plot_model(model, show_dtype=True, show_shapes=True, show_layer_names=True)" ] }, { "cell_type": "code", "execution_count": 155, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "750/750 [==============================] - 4s 5ms/step - loss: 8.1384 - mean_squared_error: 104.1044 - mean_absolute_error: 8.1384 - val_loss: 1.7779 - val_mean_squared_error: 5.0133 - val_mean_absolute_error: 1.7779\n", "Epoch 2/20\n", "750/750 [==============================] - 4s 5ms/step - loss: 8.1199 - mean_squared_error: 103.3653 - mean_absolute_error: 8.1199 - val_loss: 1.9017 - val_mean_squared_error: 5.6994 - val_mean_absolute_error: 1.9017\n", "Epoch 3/20\n", "750/750 [==============================] - 4s 5ms/step - loss: 8.0220 - mean_squared_error: 100.8974 - mean_absolute_error: 8.0220 - val_loss: 1.6635 - val_mean_squared_error: 4.4376 - val_mean_absolute_error: 1.6635\n", "Epoch 4/20\n", "750/750 [==============================] - 4s 5ms/step - loss: 7.9925 - mean_squared_error: 100.0751 - mean_absolute_error: 7.9925 - val_loss: 1.6645 - val_mean_squared_error: 4.4177 - val_mean_absolute_error: 1.6645\n", "Epoch 5/20\n", "750/750 [==============================] - 4s 5ms/step - loss: 7.9586 - mean_squared_error: 99.1430 - mean_absolute_error: 7.9586 - val_loss: 1.9522 - val_mean_squared_error: 5.9313 - val_mean_absolute_error: 1.9522\n", "Epoch 6/20\n", "750/750 [==============================] - 4s 5ms/step - loss: 7.9062 - mean_squared_error: 97.9584 - mean_absolute_error: 7.9062 - val_loss: 1.7568 - val_mean_squared_error: 4.8925 - val_mean_absolute_error: 1.7568\n", "CPU times: user 1min 7s, sys: 10.5 s, total: 1min 18s\n", "Wall time: 23.7 s\n" ] } ], "source": [ "%%time\n", "text_col, target_col = 'description_clean', 'points'\n", "\n", "early_stopping_monitor = EarlyStopping(\n", " monitor='val_mean_squared_error',\n", " min_delta=0,\n", " patience=2,\n", " verbose=0,\n", " restore_best_weights=True\n", ")\n", "\n", "history = model.fit(\n", " train_data[text_col],\n", " train_data[target_col],\n", " epochs=20,\n", " batch_size=128,\n", " verbose=1, \n", " callbacks=[early_stopping_monitor],\n", " validation_data = (test_data[text_col], test_data[target_col]))" ] }, { "cell_type": "code", "execution_count": 156, "metadata": {}, "outputs": [], "source": [ "test_data['dnn_prediction'] = model.predict(test_data[text_col])" ] }, { "cell_type": "code", "execution_count": 157, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "MSE 4.417710\n", "MAE 1.664544\n", "R2 0.539968\n", "dtype: float64" ] }, "execution_count": 157, "metadata": {}, "output_type": "execute_result" } ], "source": [ "concat_stats = calc_prediction_quality(test_data, 'dnn_prediction', target_col)\n", "concat_stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using external embedding (description feature only)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Follow https://keras.io/examples/nlp/pretrained_word_embeddings/\n", "\n", "You can either average the description words embeddings, concatinate them or do both and compare. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "TBD\n", "
" ] }, { "cell_type": "code", "execution_count": 159, "metadata": {}, "outputs": [], "source": [ "f = open(\"data/glove.6B.100d.txt\", encoding = \"utf8\")" ] }, { "cell_type": "code", "execution_count": 160, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 400000 word vectors.\n" ] } ], "source": [ "embeddings_index = {}\n", "for line in f:\n", " word, coefs = line.split(maxsplit=1)\n", " coefs = np.fromstring(coefs, \"f\", sep=\" \")\n", " embeddings_index[word] = coefs\n", "\n", "print(\"Found %s word vectors.\" % len(embeddings_index))" ] }, { "cell_type": "code", "execution_count": 161, "metadata": {}, "outputs": [], "source": [ "voc = vectorize_layer.get_vocabulary()\n", "word_index = dict(zip(voc, range(len(voc))))" ] }, { "cell_type": "code", "execution_count": 162, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Converted 19535 words (12465 misses)\n" ] } ], "source": [ "num_tokens = len(voc) + 2\n", "embedding_dim = 100\n", "hits = 0\n", "misses = 0\n", "\n", "# Prepare embedding matrix\n", "embedding_matrix = np.zeros((num_tokens, embedding_dim))\n", "for word, i in word_index.items():\n", " embedding_vector = embeddings_index.get(word)\n", " if embedding_vector is not None:\n", " # Words not found in embedding index will be all-zeros.\n", " # This includes the representation for \"padding\" and \"OOV\"\n", " embedding_matrix[i] = embedding_vector\n", " hits += 1\n", " else:\n", " misses += 1\n", "print(\"Converted %d words (%d misses)\" % (hits, misses))" ] }, { "cell_type": "code", "execution_count": 164, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "32002\n" ] }, { "data": { "text/plain": [ "32001" ] }, "execution_count": 164, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(num_tokens)\n", "len(word_index) + 1" ] }, { "cell_type": "code", "execution_count": 165, "metadata": {}, "outputs": [], "source": [ "from tensorflow import keras\n", "#from tensorflow.keras.layers import Embedding\n", "from keras.layers import Dense, Embedding, Input, Activation, Dropout, GlobalAveragePooling1D\n", "\n", "embedding_layer = Embedding(\n", " num_tokens,\n", " embedding_dim,\n", " embeddings_initializer=keras.initializers.Constant(embedding_matrix),\n", " trainable=False,\n", ")" ] }, { "cell_type": "code", "execution_count": 166, "metadata": {}, "outputs": [], "source": [ "embedding_layer = Embedding(num_tokens, embedding_dim, weights = [embedding_matrix], trainable = False) \n", "int_sequences_input = Input(shape=(1,), dtype=tf.string)\n", "vec_layer = vectorize_layer(int_sequences_input )\n", "embedded_sequences = embedding_layer(vec_layer) \n", "x = GlobalAveragePooling1D()(embedded_sequences)\n", "x = Dense(164, activation = 'relu')(x)\n", "x = Dropout(0.1)(x)\n", "output = Dense(1)(x)" ] }, { "cell_type": "code", "execution_count": 167, "metadata": {}, "outputs": [], "source": [ "pretrained_emb_model = keras.Model(inputs=int_sequences_input, outputs=output)" ] }, { "cell_type": "code", "execution_count": 168, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"model\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " input_7 (InputLayer) [(None, 1)] 0 \n", " \n", " Text_processing (TextVector (None, 60) 0 \n", " ization) \n", " \n", " embedding_1 (Embedding) (None, 60, 100) 3200200 \n", " \n", " global_average_pooling1d_3 (None, 100) 0 \n", " (GlobalAveragePooling1D) \n", " \n", " dense (Dense) (None, 164) 16564 \n", " \n", " dropout_6 (Dropout) (None, 164) 0 \n", " \n", " dense_1 (Dense) (None, 1) 165 \n", " \n", "=================================================================\n", "Total params: 3,216,929\n", "Trainable params: 16,729\n", "Non-trainable params: 3,200,200\n", "_________________________________________________________________\n" ] } ], "source": [ "pretrained_emb_model.summary()" ] }, { "cell_type": "code", "execution_count": 169, "metadata": {}, "outputs": [], "source": [ "pretrained_emb_model.compile(\n", " optimizer=tf.optimizers.Adam(), loss='mean_absolute_error', metrics=['mean_squared_error','mean_absolute_error'])" ] }, { "cell_type": "code", "execution_count": 170, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 170, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tf.keras.utils.plot_model(pretrained_emb_model, show_dtype=False, show_shapes=True, show_layer_names=True)" ] }, { "cell_type": "code", "execution_count": 173, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "750/750 [==============================] - 3s 4ms/step - loss: 24.2450 - mean_squared_error: 1150.2682 - mean_absolute_error: 24.2450 - val_loss: 11.9859 - val_mean_squared_error: 220.2822 - val_mean_absolute_error: 11.9859\n", "Epoch 2/20\n", "750/750 [==============================] - 3s 4ms/step - loss: 9.3048 - mean_squared_error: 138.7807 - mean_absolute_error: 9.3048 - val_loss: 5.3807 - val_mean_squared_error: 45.1520 - val_mean_absolute_error: 5.3807\n", "Epoch 3/20\n", "750/750 [==============================] - 3s 4ms/step - loss: 3.9033 - mean_squared_error: 25.0495 - mean_absolute_error: 3.9033 - val_loss: 1.9146 - val_mean_squared_error: 5.7946 - val_mean_absolute_error: 1.9146\n", "Epoch 4/20\n", "750/750 [==============================] - 3s 4ms/step - loss: 3.2090 - mean_squared_error: 16.2470 - mean_absolute_error: 3.2090 - val_loss: 1.8791 - val_mean_squared_error: 5.5809 - val_mean_absolute_error: 1.8791\n", "Epoch 5/20\n", "750/750 [==============================] - 3s 4ms/step - loss: 3.1596 - mean_squared_error: 15.7511 - mean_absolute_error: 3.1596 - val_loss: 1.9146 - val_mean_squared_error: 5.7983 - val_mean_absolute_error: 1.9146\n", "Epoch 6/20\n", "750/750 [==============================] - 3s 4ms/step - loss: 3.1590 - mean_squared_error: 15.7176 - mean_absolute_error: 3.1590 - val_loss: 1.8577 - val_mean_squared_error: 5.4470 - val_mean_absolute_error: 1.8577\n", "Epoch 7/20\n", "750/750 [==============================] - 3s 4ms/step - loss: 3.1385 - mean_squared_error: 15.5671 - mean_absolute_error: 3.1385 - val_loss: 1.8658 - val_mean_squared_error: 5.5301 - val_mean_absolute_error: 1.8658\n", "Epoch 8/20\n", "750/750 [==============================] - 3s 4ms/step - loss: 3.1328 - mean_squared_error: 15.5193 - mean_absolute_error: 3.1328 - val_loss: 1.8229 - val_mean_squared_error: 5.2585 - val_mean_absolute_error: 1.8229\n", "Epoch 9/20\n", "750/750 [==============================] - 3s 4ms/step - loss: 3.1223 - mean_squared_error: 15.4105 - mean_absolute_error: 3.1223 - val_loss: 1.7844 - val_mean_squared_error: 5.0316 - val_mean_absolute_error: 1.7844\n", "Epoch 10/20\n", "750/750 [==============================] - 3s 4ms/step - loss: 3.0949 - mean_squared_error: 15.1229 - mean_absolute_error: 3.0949 - val_loss: 1.8556 - val_mean_squared_error: 5.4600 - val_mean_absolute_error: 1.8556\n", "Epoch 11/20\n", "750/750 [==============================] - 3s 4ms/step - loss: 3.1058 - mean_squared_error: 15.1846 - mean_absolute_error: 3.1058 - val_loss: 1.8283 - val_mean_squared_error: 5.2993 - val_mean_absolute_error: 1.8283\n", "CPU times: user 59.7 s, sys: 7.33 s, total: 1min 7s\n", "Wall time: 31.5 s\n" ] } ], "source": [ "%%time\n", "text_col, target_col = 'description_clean', 'points'\n", "\n", "early_stopping_monitor = EarlyStopping(\n", " monitor='val_loss',\n", " min_delta=0,\n", " patience=2,\n", " verbose=0,\n", " restore_best_weights=True\n", ")\n", "\n", "history = pretrained_emb_model.fit(\n", " train_data[text_col],\n", " train_data[target_col],\n", " epochs=20,\n", " batch_size=128,\n", " verbose=1, \n", " callbacks=[early_stopping_monitor],\n", " validation_data = (test_data[text_col], test_data[target_col]))" ] }, { "cell_type": "code", "execution_count": 174, "metadata": {}, "outputs": [], "source": [ "test_data['ext_emb_prediction'] = pretrained_emb_model.predict(test_data[text_col])" ] }, { "cell_type": "code", "execution_count": 176, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "MSE 5.031617\n", "MAE 1.784432\n", "R2 0.476040\n", "dtype: float64" ] }, "execution_count": 176, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ext_emb_stats = calc_prediction_quality(test_data, 'ext_emb_prediction', target_col)\n", "ext_emb_stats" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using LSTM or RNN Layer, (description feature only)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See example here: https://www.tensorflow.org/api_docs/python/tf/keras/layers/LSTM" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "TBD\n", "
" ] }, { "cell_type": "code", "execution_count": 178, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow.keras.layers import LSTM, Dense, Dropout, Embedding\n", "from tensorflow.keras import Sequential" ] }, { "cell_type": "code", "execution_count": 179, "metadata": {}, "outputs": [], "source": [ "embedding_dim=16\n", "units=160\n", "\n", "lstm_model = tf.keras.Sequential([\n", " tf.keras.Input(shape=(1,), dtype=tf.string),\n", " vectorize_layer,\n", " Embedding(vocab_size, embedding_dim, name=\"embedding\", ),\n", " LSTM(units),\n", " Dense(164, activation='relu', name='hidden_layer'),\n", " Dropout(0.2),\n", " Dense(1, name = 'output_layer')\n", "])" ] }, { "cell_type": "code", "execution_count": 180, "metadata": {}, "outputs": [], "source": [ "lstm_model.compile(\n", " optimizer=tf.optimizers.Adam(), loss='mean_absolute_error', metrics=['mean_squared_error','mean_absolute_error'])" ] }, { "cell_type": "code", "execution_count": 181, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_6\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " Text_processing (TextVector (None, 60) 0 \n", " ization) \n", " \n", " embedding (Embedding) (None, 60, 16) 512000 \n", " \n", " lstm (LSTM) (None, 160) 113280 \n", " \n", " hidden_layer (Dense) (None, 164) 26404 \n", " \n", " dropout_7 (Dropout) (None, 164) 0 \n", " \n", " output_layer (Dense) (None, 1) 165 \n", " \n", "=================================================================\n", "Total params: 651,849\n", "Trainable params: 651,849\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "lstm_model.summary()" ] }, { "cell_type": "code", "execution_count": 182, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "750/750 [==============================] - 76s 98ms/step - loss: 7.6057 - mean_squared_error: 250.0398 - mean_absolute_error: 7.6057 - val_loss: 2.5693 - val_mean_squared_error: 9.9154 - val_mean_absolute_error: 2.5693\n", "Epoch 2/20\n", "750/750 [==============================] - 74s 99ms/step - loss: 4.5949 - mean_squared_error: 33.0060 - mean_absolute_error: 4.5949 - val_loss: 2.5188 - val_mean_squared_error: 9.7013 - val_mean_absolute_error: 2.5188\n", "Epoch 3/20\n", "750/750 [==============================] - 73s 97ms/step - loss: 4.5924 - mean_squared_error: 33.0083 - mean_absolute_error: 4.5924 - val_loss: 2.5337 - val_mean_squared_error: 9.5674 - val_mean_absolute_error: 2.5337\n", "Epoch 4/20\n", "750/750 [==============================] - 75s 100ms/step - loss: 4.6053 - mean_squared_error: 33.1895 - mean_absolute_error: 4.6053 - val_loss: 2.6168 - val_mean_squared_error: 10.5421 - val_mean_absolute_error: 2.6168\n", "CPU times: user 13min 18s, sys: 4min 42s, total: 18min 1s\n", "Wall time: 4min 57s\n" ] } ], "source": [ "%%time\n", "text_col, target_col = 'description_clean', 'points'\n", "\n", "early_stopping_monitor = EarlyStopping(\n", " monitor='val_loss',\n", " min_delta=0,\n", " patience=2,\n", " verbose=0,\n", " restore_best_weights=True\n", ")\n", "\n", "history = lstm_model.fit(\n", " train_data[text_col],\n", " train_data[target_col],\n", " epochs=20,\n", " batch_size=128,\n", " verbose=1, \n", " callbacks=[early_stopping_monitor],\n", " validation_data = (test_data[text_col], test_data[target_col]))" ] }, { "cell_type": "code", "execution_count": 183, "metadata": {}, "outputs": [], "source": [ "test_data['lstm_prediction'] = lstm_model.predict(test_data[text_col])" ] }, { "cell_type": "code", "execution_count": 185, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "MSE 9.701329\n", "MAE 2.518778\n", "R2 -0.010234\n", "dtype: float64" ] }, "execution_count": 185, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lstm_stats = calc_prediction_quality(test_data, 'lstm_prediction', target_col)\n", "lstm_stats" ] }, { "cell_type": "code", "execution_count": 186, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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MSEMAER2
baseline_19.6032182.537841-0.000018
baseline_29.1301902.4718400.049241
baseline_38.5894862.3763680.105546
regression_by_xgb6.4102841.9078260.332474
nlp3.0094921.3676810.686611
nlp_all3.0134341.3685120.686200
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dnn_concat4.4177101.6645440.539968
lstm9.7013292.518778-0.010234
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" ], "text/plain": [ " MSE MAE R2\n", "baseline_1 9.603218 2.537841 -0.000018\n", "baseline_2 9.130190 2.471840 0.049241\n", "baseline_3 8.589486 2.376368 0.105546\n", "regression_by_xgb 6.410284 1.907826 0.332474\n", "nlp 3.009492 1.367681 0.686611\n", "nlp_all 3.013434 1.368512 0.686200\n", "dnn 3.052601 1.379098 0.682122\n", "dnn_concat 4.417710 1.664544 0.539968\n", "lstm 9.701329 2.518778 -0.010234" ] }, "execution_count": 186, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_compared = pd.DataFrame([b1_stats, b2_stats, b3_stats, xgb_stats, bow_stats, bowf_stats, dnn_stats, concat_stats, lstm_stats], index=['baseline_1', 'baseline_2','baseline_3','regression_by_xgb','nlp', 'nlp_all', 'dnn','dnn_concat','lstm'])\n", "all_compared" ] }, { "cell_type": "code", "execution_count": 187, "metadata": {}, "outputs": [], "source": [ "all_compared.to_csv('data/all_models_compared.csv', index=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bonus (Not mandatory) Use all features using Keras functional API" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See here: https://keras.io/guides/functional_api/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "TBD\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select encoded features for train" ] }, { "cell_type": "code", "execution_count": 188, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countryprovinceregion_1region_2taster_namevarietywineryprice
1321087386164511295615.0
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" ], "text/plain": [ " country province region_1 region_2 taster_name variety winery price\n", "1 32 108 738 6 16 451 12956 15.0" ] }, "execution_count": 188, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_encoded = train_encoded_features.iloc[:,:-1]\n", "train_encoded.head(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select encoded features for test" ] }, { "cell_type": "code", "execution_count": 190, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countryprovinceregion_1region_2taster_namevarietywineryprice
0223324246969111608-1.0
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" ], "text/plain": [ " country province region_1 region_2 taster_name variety winery price\n", "0 22 332 424 6 9 691 11608 -1.0" ] }, "execution_count": 190, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_encoded = test_encoded_features.iloc[:,:-2]\n", "test_encoded.head(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "importing libraries" ] }, { "cell_type": "code", "execution_count": 191, "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras import Model\n", "from tensorflow.keras.layers import TextVectorization, Embedding, LSTM, Dense, GlobalAveragePooling1D, Dropout, Input\n", "from tensorflow.keras.callbacks import EarlyStopping\n", "from keras.layers.merge import concatenate" ] }, { "cell_type": "code", "execution_count": 192, "metadata": {}, "outputs": [], "source": [ "text_features_input = Input(shape=(1,), dtype=tf.string)\n", "vect_layer = vectorize_layer(text_features_input)\n", "embeddding_layer = Embedding(vocab_size, embedding_dim, name=\"embedding\", )(vect_layer)\n", "concat_layer = Reshape((embedding_dim * sequence_length, ), name='concat_words')(embeddding_layer)\n", "hidden_layer1 = Dense(200, activation='relu', name='hidden_layer1')(concat_layer)\n", "\n", "country_input = Input(shape = (1,))\n", "province_input = Input(shape = (1,))\n", "region_1_input = Input(shape = (1,))\n", "region_2_input = Input(shape = (1,))\n", "taster_name_input = Input(shape = (1,))\n", "variety_input = Input(shape = (1,))\n", "winery_input = Input(shape = (1,))\n", "price_input = Input(shape = (1,))\n", "\n", "other_features = concatenate([country_input,province_input,region_1_input,region_2_input,taster_name_input,variety_input,winery_input,price_input])\n", "\n", "hidden_layer2 = Dense(200, activation='relu', name='hidden_layer2')(other_features)\n", "\n", "merge = concatenate([hidden_layer1,hidden_layer2])\n", "\n", "hidden_layer3 = Dense(200, activation='relu', name='hidden_layer3')(merge)\n", "reg_layer = Dropout(0.3)(hidden_layer3)\n", "output = Dense(1)(reg_layer)" ] }, { "cell_type": "code", "execution_count": 193, "metadata": {}, "outputs": [], "source": [ "api_model = Model(inputs = [text_features_input,country_input,province_input,region_1_input,region_2_input,taster_name_input,variety_input,winery_input,price_input] , outputs = output)\n" ] }, { "cell_type": "code", "execution_count": 194, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"model_1\"\n", "__________________________________________________________________________________________________\n", " Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", " input_10 (InputLayer) [(None, 1)] 0 [] \n", " \n", " Text_processing (TextVectoriza (None, 60) 0 ['input_10[0][0]'] \n", " tion) \n", " \n", " embedding (Embedding) (None, 60, 16) 512000 ['Text_processing[4][0]'] \n", " \n", " input_11 (InputLayer) [(None, 1)] 0 [] \n", " \n", " input_12 (InputLayer) [(None, 1)] 0 [] \n", " \n", " input_13 (InputLayer) [(None, 1)] 0 [] \n", " \n", " input_14 (InputLayer) [(None, 1)] 0 [] \n", " \n", " input_15 (InputLayer) [(None, 1)] 0 [] \n", " \n", " input_16 (InputLayer) [(None, 1)] 0 [] \n", " \n", " input_17 (InputLayer) [(None, 1)] 0 [] \n", " \n", " input_18 (InputLayer) [(None, 1)] 0 [] \n", " \n", " concat_words (Reshape) (None, 960) 0 ['embedding[0][0]'] \n", " \n", " concatenate (Concatenate) (None, 8) 0 ['input_11[0][0]', \n", " 'input_12[0][0]', \n", " 'input_13[0][0]', \n", " 'input_14[0][0]', \n", " 'input_15[0][0]', \n", " 'input_16[0][0]', \n", " 'input_17[0][0]', \n", " 'input_18[0][0]'] \n", " \n", " hidden_layer1 (Dense) (None, 200) 192200 ['concat_words[0][0]'] \n", " \n", " hidden_layer2 (Dense) (None, 200) 1800 ['concatenate[0][0]'] \n", " \n", " concatenate_1 (Concatenate) (None, 400) 0 ['hidden_layer1[0][0]', \n", " 'hidden_layer2[0][0]'] \n", " \n", " hidden_layer3 (Dense) (None, 200) 80200 ['concatenate_1[0][0]'] \n", " \n", " dropout_8 (Dropout) (None, 200) 0 ['hidden_layer3[0][0]'] \n", " \n", " dense_2 (Dense) (None, 1) 201 ['dropout_8[0][0]'] \n", " \n", "==================================================================================================\n", "Total params: 786,401\n", "Trainable params: 786,401\n", "Non-trainable params: 0\n", "__________________________________________________________________________________________________\n" ] } ], "source": [ "api_model.summary()" ] }, { "cell_type": "code", "execution_count": 195, "metadata": {}, "outputs": [], "source": [ "api_model.compile(\n", " optimizer=tf.optimizers.Adam(), loss='mean_absolute_error', metrics=['mean_squared_error','mean_absolute_error'])" ] }, { "cell_type": "code", "execution_count": 196, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "execution_count": 196, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tf.keras.utils.plot_model(api_model, show_dtype=True, show_shapes=True, show_layer_names=True)\n" ] }, { "cell_type": "code", "execution_count": 197, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(95990, 8)" ] }, "execution_count": 197, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_encoded.shape" ] }, { "cell_type": "code", "execution_count": 199, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(95990, 14)" ] }, "execution_count": 199, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_data.shape" ] }, { "cell_type": "code", "execution_count": 200, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(23998, 8)" ] }, "execution_count": 200, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_encoded.shape" ] }, { "cell_type": "code", "execution_count": 201, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(23998, 21)" ] }, "execution_count": 201, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_data.shape" ] }, { "cell_type": "code", "execution_count": 202, "metadata": {}, "outputs": [], "source": [ "text_train = train_data['description_clean']\n", "country_train = train_encoded['country']\n", "province_train = train_encoded['province']\n", "region_1_train = train_encoded['region_1']\n", "region_2_train = train_encoded['region_2']\n", "taster_name_train = train_encoded['taster_name']\n", "variety_train = train_encoded['variety']\n", "winery_train = train_encoded['winery']\n", "price_train = train_encoded['price']" ] }, { "cell_type": "code", "execution_count": 203, "metadata": {}, "outputs": [], "source": [ "text_test = test_data['description_clean']\n", "country_test = test_encoded['country']\n", "province_test = test_encoded['province']\n", "region_1_test = test_encoded['region_1']\n", "region_2_test= test_encoded['region_2']\n", "taster_name_test = test_encoded['taster_name']\n", "variety_test = test_encoded['variety']\n", "winery_test = test_encoded['winery']\n", "price_test = test_encoded['price']" ] }, { "cell_type": "code", "execution_count": 205, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "750/750 [==============================] - 7s 9ms/step - loss: 19.7052 - mean_squared_error: 3651.8381 - mean_absolute_error: 19.7052 - val_loss: 8.3475 - val_mean_squared_error: 85.8282 - val_mean_absolute_error: 8.3475\n", "Epoch 2/20\n", "750/750 [==============================] - 6s 8ms/step - loss: 9.4897 - mean_squared_error: 141.9702 - mean_absolute_error: 9.4897 - val_loss: 3.5836 - val_mean_squared_error: 19.7594 - val_mean_absolute_error: 3.5836\n", "Epoch 3/20\n", "750/750 [==============================] - 7s 9ms/step - loss: 8.7450 - mean_squared_error: 120.2302 - mean_absolute_error: 8.7450 - val_loss: 2.3589 - val_mean_squared_error: 8.9636 - val_mean_absolute_error: 2.3589\n", "Epoch 4/20\n", "750/750 [==============================] - 6s 9ms/step - loss: 8.5034 - mean_squared_error: 113.8297 - mean_absolute_error: 8.5034 - val_loss: 2.4142 - val_mean_squared_error: 9.2032 - val_mean_absolute_error: 2.4142\n", "Epoch 5/20\n", "750/750 [==============================] - 6s 8ms/step - loss: 8.3717 - mean_squared_error: 110.3284 - mean_absolute_error: 8.3717 - val_loss: 2.3949 - val_mean_squared_error: 9.1352 - val_mean_absolute_error: 2.3949\n", "CPU times: user 1min 20s, sys: 28.9 s, total: 1min 49s\n", "Wall time: 32.6 s\n" ] } ], "source": [ "%%time\n", "text_col, target_col = 'description_clean', 'points'\n", "\n", "early_stopping_monitor = EarlyStopping(\n", " monitor='val_mean_squared_error',\n", " min_delta=0,\n", " patience=2,\n", " verbose=0,\n", " restore_best_weights=True\n", ")\n", "\n", "history = api_model.fit(\n", " [text_train,country_train,province_train,region_1_train,region_2_train,taster_name_train,variety_train,winery_train,price_train],\n", " train_data[target_col],\n", " epochs=20,\n", " batch_size=128,\n", " verbose=1, \n", " callbacks=[early_stopping_monitor],\n", " validation_data = ([text_test,country_test,province_test,region_1_test,region_2_test,taster_name_test,variety_test,\\\n", " winery_test,price_test], test_data[target_col]))" ] }, { "cell_type": "code", "execution_count": 207, "metadata": {}, "outputs": [], "source": [ "test_data['api_prediction'] = api_model.predict([text_test,country_test,province_test,region_1_test,region_2_test,\\\n", " taster_name_test,variety_test,winery_test,price_test])" ] }, { "cell_type": "code", "execution_count": 209, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "MSE 8.963567\n", "MAE 2.358948\n", "R2 0.066592\n", "dtype: float64" ] }, "execution_count": 209, "metadata": {}, "output_type": "execute_result" } ], "source": [ "api_stats = calc_prediction_quality(test_data, 'api_prediction', target_col)\n", "api_stats" ] }, { "cell_type": "code", "execution_count": 211, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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MSEMAER2
baseline_19.6032182.537841-0.000018
baseline_29.1301902.4718400.049241
baseline_38.5894862.3763680.105546
regression_by_xgb6.4102841.9078260.332474
nlp3.0094921.3676810.686611
nlp_all3.0134341.3685120.686200
dnn3.0526011.3790980.682122
dnn_concat4.4177101.6645440.539968
lstm9.7013292.518778-0.010234
functional_api8.9635672.3589480.066592
\n", "
" ], "text/plain": [ " MSE MAE R2\n", "baseline_1 9.603218 2.537841 -0.000018\n", "baseline_2 9.130190 2.471840 0.049241\n", "baseline_3 8.589486 2.376368 0.105546\n", "regression_by_xgb 6.410284 1.907826 0.332474\n", "nlp 3.009492 1.367681 0.686611\n", "nlp_all 3.013434 1.368512 0.686200\n", "dnn 3.052601 1.379098 0.682122\n", "dnn_concat 4.417710 1.664544 0.539968\n", "lstm 9.701329 2.518778 -0.010234\n", "functional_api 8.963567 2.358948 0.066592" ] }, "execution_count": 211, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_compared = pd.DataFrame([b1_stats, b2_stats, b3_stats, xgb_stats, bow_stats, bowf_stats, dnn_stats, concat_stats,\\\n", " lstm_stats, api_stats], index=['baseline_1', 'baseline_2','baseline_3','regression_by_xgb',\\\n", " 'nlp', 'nlp_all', 'dnn','dnn_concat','lstm', 'functional_api'])\n", "all_compared" ] }, { "cell_type": "code", "execution_count": 212, "metadata": {}, "outputs": [], "source": [ "all_compared.to_csv('data/all_models_compared.csv', index=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conclusion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "according to both MSE and MAE, most basic NLP with just the description gives the best results." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 4 }